冲突感知的主动自动机学习
Conflict-Aware Active Automata Learning
AI总结：
针对主动自动机学习难以处理观测冲突、在噪声或系统变异场景适用性受限的问题，提出C3AL框架，将观测树作为核心要素，适配任意现有学习器并减少测试次数，经大规模基准测试验证其在黑盒学习中处理噪声与变异的优势。
摘要：
主动自动机学习算法难以处理观测数据中的冲突（即相同输入观测到不同输出）。这种冲突后无法恢复的固有缺陷，削弱了其在存在噪声或待学习系统发生变异场景下的实际适用性。我们提出冲突感知主动自动机学习（C3AL）框架，以实现学习过程中冲突信息的处理。其核心思想是将所谓的观测树作为学习过程中的一等公民。尽管这一思路在近期研究中已有探索，但我们通过使其可适配任意现有学习器，并最大程度减少对待学习系统的测试次数（尤其是在面临冲突时），将其效用发挥到极致。我们在包含30多个不同真实目标、18000多种不同场景的大规模基准测试集上评估了C3AL。评估结果表明，C3AL是黑盒学习的一种合适替代框架，能更好地处理噪声和系统变异问题。
英文摘要：
Active automata learning algorithms cannot easily handle conflict in the observation data (different outputs observed for the same inputs). This inherent inability to recover after a conflict impairs their effective applicability in scenarios where noise is present or the system under learning is mutating. We propose the Conflict-Aware Active Automata Learning (C3AL) framework to enable handling conflicting information during the learning process. The core idea is to consider the so-called observation tree as a first-class citizen in the learning process. Though this idea is explored in recent work, we take it to its full effect by enabling its use with any existing learner and minimizing the number of tests performed on the system under learning, specially in the face of conflicts. We evaluate C3AL in a large set of benchmarks, covering over 30 different realistic targets, and over 18,000 different scenarios. The results of the evaluation show that C3AL is a suitable alternative framework for closed-box learning that can better handle noise and mutations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
